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Influenza A viruses cause annual influenza epidemics and occasional severe pandemics. Their genome is segmented into eight fragments, which offers evolutionary advantages but complicates genomic packaging. The existence of a selective packaging mechanism, in which one copy of each viral RNA is specifically packaged into each virion, is suspected, but its molecular details remain unknown. Here, we identified a direct intermolecular interaction between two viral genomic RNA segments of an avian influenza A virus using in vitro experiments. Using silent trans -complementary mutants, we then demonstrated that this interaction takes place in infected cells and is required for optimal viral replication. Disruption of this interaction did not affect the HA titer of the mutant viruses, suggesting that the same amount of viral particles was produced. However, it nonspecifically decreased the amount of viral RNA in the viral particles, resulting in an eightfold increase in empty viral particles. Competition experiments indicated that this interaction favored copackaging of the interacting viral RNA segments. The interaction we identified involves regions not previously designated as packaging signals and is not widely conserved among influenza A virus. Combined with previous studies, our experiments indicate that viral RNA segments can promote the selective packaging of the influenza A virus genome by forming a sequence-dependent supramolecular network of interactions. The lack of conservation of these interactions might limit genetic reassortment between divergent influenza A viruses.

The fragmented nature of the influenza A genome allows the exchange of gene segments when two or more influenza viruses infect the same cell, but little is known about the rules underlying this process. Here, we studied genetic reassortment between the A/Moscow/10/99 (H3N2, MO) virus originally isolated from human and the avian A/Finch/England/2051/91 (H5N2, EN) virus and found that this process is strongly biased. Importantly, the avian HA segment never entered the MO genetic background alone but always was accompanied by the avian PA and M fragments. Introduction of the 5′ and 3′ packaging sequences of HA MO into an otherwise HA EN backbone allowed efficient incorporation of the chimerical viral RNA (vRNA) into the MO genetic background. Furthermore, forcing the incorporation of the avian M segment or introducing five silent mutations into the human M segment was sufficient to drive coincorporation of the avian HA segment into the MO genetic background. These silent mutations also strongly affected the genotype of reassortant viruses. Taken together, our results indicate that packaging signals are crucial for genetic reassortment and that suboptimal compatibility between the vRNA packaging signals, which are detected only when vRNAs compete for packaging, limit this process.

The term “juvenile-like (inflammatory/hyperplastic) mucosal polyps” (JLIHMP) has been recently introduced to describe a spectrum of polypoid lesions in patients with neurofibromatosis type 1 (NF-1). Due to the scarce number of reported cases and histopathological similarities with entities such as sporadic/syndromic juvenile polyps or inflammatory fibroid polyps, this entity remains a subject of debate. We describe herein a case of multiple JLIHMPs in a patient with NF-1, and we document the presence of low-grade dysplasia within one of these polyps.

We present a novel high-resolution inverse modelling system (“FLEXVAR”) based on FLEXPART-COSMO back trajectories driven by COSMO meteorological fields at 7km×7km resolution over the European COSMO-7 domain and the four-dimensional variational (4DVAR) data assimilation technique. FLEXVAR is coupled offline with the global inverse modelling system TM5-4DVAR to provide background mole fractions (“baselines”) consistent with the global observations assimilated in TM5-4DVAR. We have applied the FLEXVAR system for the inverse modelling of European CH4 emissions in 2018 using 24 stations with in situ measurements, complemented with data from five stations with discrete air sampling (and additional stations outside the European COSMO-7 domain used for the global TM5-4DVAR inversions). The sensitivity of the FLEXVAR inversions to different approaches to calculate the baselines, different parameterizations of the model representation error, different settings of the prior error covariance parameters, different prior inventories, and different observation data sets are investigated in detail. Furthermore, the FLEXVAR inversions are compared to inversions with the FLEXPART extended Kalman filter (“FLExKF”) system and with TM5-4DVAR inversions at 1∘×1∘ resolution over Europe. The three inverse modelling systems show overall good consistency of the major spatial patterns of the derived inversion increments and in general only relatively small differences in the derived annual total emissions of larger country regions. At the same time, the FLEXVAR inversions at 7km×7km resolution allow the observations to be better reproduced than the TM5-4DVAR simulations at 1∘×1∘. The three inverse models derive higher annual total CH4 emissions in 2018 for Germany, France, and BENELUX compared to the sum of anthropogenic emissions reported to UNFCCC and natural emissions estimated from the Global Carbon Project CH4 inventory, but the uncertainty ranges of top-down and bottom-up total emission estimates overlap for all three country regions. In contrast, the top-down estimates for the sum of emissions from the UK and Ireland agree relatively well with the total of anthropogenic and natural bottom-up inventories.

Facility-scale methane emission fluxes can be derived by comparing tracer and methane mole fraction measurements downwind of a methane emission source, where a co-located tracer gas is released at a known flux rate. Acetylene is a commonly used methane tracer due to its availability, low cost and low atmospheric background. Acetylene mole fraction can be measured using infrared gas analysers such as the cavity ring-down spectroscopy Picarro G2203. However, failure to calibrate tracer gas analysers may influence methane flux estimation, due to inaccurate raw tracer mole fraction measurements. We conducted extensive Picarro G2203 laboratory characterisation testing. Picarro G2203 acetylene measurements were calibrated by diluting a high concentration of acetylene with ambient air. The precise level of acetylene in each dilution blend was determined by diluting a high-concentration methane source in an identical way, with reliable methane mole fraction measurements used to quantify the true level of dilution. A linear calibration fit applied to raw acetylene mole fraction measured by the Picarro G2203 showed that these measurements could be corrected through direct multiplication with a calibration gain factor of 0.94. However, this specific calibration for the Picarro G2203 tested in this study is only valid from an acetylene mole fraction of 1.16 ppb, below which unstable measurements were observed. The same Picarro G2203 was used during a field study to perform 14 successful transects downwind of an active landfill site, where a point-source acetylene release was conducted at a fixed flow rate. Methane fluxes were derived by integrating the methane and acetylene mole fraction plumes, as a function of distance along the sampling road. This resulted in a ±56 % flux variability between different transects which was principally due to errors associated with the tracer release location and downwind sampling positioning. Methane fluxes were also derived using raw uncalibrated Picarro G2203 acetylene mole fraction instead of calibrated measurements, which resulted an average methane emission flux underestimation of approximately 8 % for this specific study, compared to fluxes derived using calibrated measurements. Unlike a random uncertainty, this bias represents a consistent flux underestimation that cannot be reduced by improving the field sampling methodology; the only solution is using calibrated acetylene mole fraction measurements. The magnitude of the bias is principally due to the 0.94 multiplicative gain factor. Therefore, a similar level of methane flux bias can be expected in other studies when using uncalibrated acetylene mole fraction measurements from the Picarro G2203 tested in this work. This study therefore emphasises the equal importance of calibrating target as well as tracer gas measurements, regardless of the instrument being used to obtain these measurements. Otherwise, biases can be induced within target gas flux estimates. For the example of methane, this can influence our understanding of the role of certain facility-scale sources within the global methane budget.

We present a novel high-resolution inverse modelling system (\"FLEXVAR\") based on FLEXPARTCOSMO back trajectories driven by COSMO meteorological fields at 7 km×7 km resolution over the European COSMO-7 domain and the four-dimensional variational (4DVAR) data assimilation technique. FLEXVAR is coupled offline with the global inverse modelling system TM5-4DVAR to provide background mole fractions (\"baselines\") consistent with the global observations assimilated in TM5-4DVAR. We have applied the FLEXVAR system for the inverse modelling of European CH4 emissions in 2018 using 24 stations with in situ measurements, complemented with data from five stations with discrete air sampling (and additional stations outside the European COSMO-7 domain used for the global TM5-4DVAR inversions). The sensitivity of the FLEXVAR inversions to different approaches to calculate the baselines, different parameterizations of the model representation error, different settings of the prior error covariance parameters, different prior inventories, and different observation data sets are investigated in detail. Furthermore, the FLEXVAR inversions are compared to inversions with the FLEXPART extended Kalman filter (\"FLExKF\") system and with TM5-4DVAR inversions at 1° × 1° resolution over Europe. The three inverse modelling systems show overall good consistency of the major spatial patterns of the derived inversion increments and in general only relatively small differences in the derived annual total emissions of larger country regions. At the same time, the FLEXVAR inversions at 7 km × 7 km resolution allow the observations to be better reproduced than the TM5-4DVAR simulations at 1° × 1°. The three inverse models derive higher annual total CH4 emissions in 2018 for Germany, France, and BENELUX compared to the sum of anthropogenic emissions reported to UNFCCC and natural emissions estimated from the Global Carbon Project CH4 inventory, but the uncertainty ranges of top-down and bottom-up total emission estimates overlap for all three country regions. In contrast, the top-down estimates for the sum of emissions from the UK and Ireland agree relatively well with the total of anthropogenic and natural bottom-up inventories.

We present a novel high-resolution inverse modelling system (\"FLEXVAR\") based on FLEXPART-COSMO back trajectories driven by COSMO meteorological fields at 7 kmx7 km resolution over the European COSMO-7 domain and the four-dimensional variational (4DVAR) data assimilation technique. FLEXVAR is coupled offline with the global inverse modelling system TM5-4DVAR to provide background mole fractions (\"baselines\") consistent with the global observations assimilated in TM5-4DVAR. We have applied the FLEXVAR system for the inverse modelling of European CH.sub.4 emissions in 2018 using 24 stations with in situ measurements, complemented with data from five stations with discrete air sampling (and additional stations outside the European COSMO-7 domain used for the global TM5-4DVAR inversions). The sensitivity of the FLEXVAR inversions to different approaches to calculate the baselines, different parameterizations of the model representation error, different settings of the prior error covariance parameters, different prior inventories, and different observation data sets are investigated in detail. Furthermore, the FLEXVAR inversions are compared to inversions with the FLEXPART extended Kalman filter (\"FLExKF\") system and with TM5-4DVAR inversions at 1\"x1\" resolution over Europe. The three inverse modelling systems show overall good consistency of the major spatial patterns of the derived inversion increments and in general only relatively small differences in the derived annual total emissions of larger country regions. At the same time, the FLEXVAR inversions at 7 kmx7 km resolution allow the observations to be better reproduced than the TM5-4DVAR simulations at 1\"x1\". The three inverse models derive higher annual total CH.sub.4 emissions in 2018 for Germany, France, and BENELUX compared to the sum of anthropogenic emissions reported to UNFCCC and natural emissions estimated from the Global Carbon Project CH.sub.4 inventory, but the uncertainty ranges of top-down and bottom-up total emission estimates overlap for all three country regions. In contrast, the top-down estimates for the sum of emissions from the UK and Ireland agree relatively well with the total of anthropogenic and natural bottom-up inventories.

We present a novel high-resolution inverse modelling system (“FLEXVAR”) based on FLEXPART-COSMO back trajectories driven by COSMO meteorological fields at 7 km×7 km resolution over the European COSMO-7 domain and the four-dimensional variational (4DVAR) data assimilation technique. FLEXVAR is coupled offline with the global inverse modelling system TM5-4DVAR to provide background mole fractions (“baselines”) consistent with the global observations assimilated in TM5-4DVAR. We have applied the FLEXVAR system for the inverse modelling of European CH4 emissions in 2018 using 24 stations with in situ measurements, complemented with data from five stations with discrete air sampling (and additional stations outside the European COSMO-7 domain used for the global TM5-4DVAR inversions). The sensitivity of the FLEXVAR inversions to different approaches to calculate the baselines, different parameterizations of the model representation error, different settings of the prior error covariance parameters, different prior inventories, and different observation data sets are investigated in detail. Furthermore, the FLEXVAR inversions are compared to inversions with the FLEXPART extended Kalman filter (“FLExKF”) system and with TM5-4DVAR inversions at 1∘×1∘ resolution over Europe. The three inverse modelling systems show overall good consistency of the major spatial patterns of the derived inversion increments and in general only relatively small differences in the derived annual total emissions of larger country regions. At the same time, the FLEXVAR inversions at 7 km×7 km resolution allow the observations to be better reproduced than the TM5-4DVAR simulations at 1∘×1∘. The three inverse models derive higher annual total CH4 emissions in 2018 for Germany, France, and BENELUX compared to the sum of anthropogenic emissions reported to UNFCCC and natural emissions estimated from the Global Carbon Project CH4 inventory, but the uncertainty ranges of top-down and bottom-up total emission estimates overlap for all three country regions. In contrast, the top-down estimates for the sum of emissions from the UK and Ireland agree relatively well with the total of anthropogenic and natural bottom-up inventories.